Jade Copet


2022

pdf bib
textless-lib: a Library for Textless Spoken Language Processing
Eugene Kharitonov | Jade Copet | Kushal Lakhotia | Tu Anh Nguyen | Paden Tomasello | Ann Lee | Ali Elkahky | Wei-Ning Hsu | Abdelrahman Mohamed | Emmanuel Dupoux | Yossi Adi
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations

Textless spoken language processing is an exciting area of research that promises to extend applicability of the standard NLP toolset onto spoken language and languages with few or no textual resources.Here, we introduce textless-lib, a PyTorch-based library aimed to facilitate research in the area. We describe the building blocks that the library provides and demonstrate its usability by discuss three different use-case examples: (i) speaker probing, (ii) speech resynthesis and compression, and (iii) speech continuation. We believe that textless-lib substantially simplifies research the textless setting and will be handful not only for speech researchers but also for the NLP community at large.

pdf
Text-Free Prosody-Aware Generative Spoken Language Modeling
Eugene Kharitonov | Ann Lee | Adam Polyak | Yossi Adi | Jade Copet | Kushal Lakhotia | Tu Anh Nguyen | Morgane Riviere | Abdelrahman Mohamed | Emmanuel Dupoux | Wei-Ning Hsu
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Speech pre-training has primarily demonstrated efficacy on classification tasks, while its capability of generating novel speech, similar to how GPT-2 can generate coherent paragraphs, has barely been explored. Generative Spoken Language Modeling (GSLM) (CITATION) is the only prior work addressing the generative aspect of speech pre-training, which builds a text-free language model using discovered units. Unfortunately, because the units used in GSLM discard most prosodic information, GSLM fails to leverage prosody for better comprehension and does not generate expressive speech. In this work, we present a prosody-aware generative spoken language model (pGSLM). It is composed of a multi-stream transformer language model (MS-TLM) of speech, represented as discovered unit and prosodic feature streams, and an adapted HiFi-GAN model converting MS-TLM outputs to waveforms. Experimental results show that the pGSLM can utilize prosody to improve both prosody and content modeling, and also generate natural, meaningful, and coherent speech given a spoken prompt. Audio samples can be found at https://speechbot.github.io/pgslm. Codes and models are available at https://github.com/pytorch/fairseq/tree/main/examples/textless_nlp/pgslm.

pdf
Textless Speech Emotion Conversion using Discrete & Decomposed Representations
Felix Kreuk | Adam Polyak | Jade Copet | Eugene Kharitonov | Tu Anh Nguyen | Morgan Rivière | Wei-Ning Hsu | Abdelrahman Mohamed | Emmanuel Dupoux | Yossi Adi
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Speech emotion conversion is the task of modifying the perceived emotion of a speech utterance while preserving the lexical content and speaker identity. In this study, we cast the problem of emotion conversion as a spoken language translation task. We use a decomposition of the speech signal into discrete learned representations, consisting of phonetic-content units, prosodic features, speaker, and emotion. First, we modify the speech content by translating the phonetic-content units to a target emotion, and then predict the prosodic features based on these units. Finally, the speech waveform is generated by feeding the predicted representations into a neural vocoder. Such a paradigm allows us to go beyond spectral and parametric changes of the signal, and model non-verbal vocalizations, such as laughter insertion, yawning removal, etc. We demonstrate objectively and subjectively that the proposed method is vastly superior to current approaches and even beats text-based systems in terms of perceived emotion and audio quality. We rigorously evaluate all components of such a complex system and conclude with an extensive model analysis and ablation study to better emphasize the architectural choices, strengths and weaknesses of the proposed method. Samples are available under the following link: https://speechbot.github.io/emotion

2021

pdf
On Generative Spoken Language Modeling from Raw Audio
Kushal Lakhotia | Eugene Kharitonov | Wei-Ning Hsu | Yossi Adi | Adam Polyak | Benjamin Bolte | Tu-Anh Nguyen | Jade Copet | Alexei Baevski | Abdelrahman Mohamed | Emmanuel Dupoux
Transactions of the Association for Computational Linguistics, Volume 9

Abstract We introduce Generative Spoken Language Modeling, the task of learning the acoustic and linguistic characteristics of a language from raw audio (no text, no labels), and a set of metrics to automatically evaluate the learned representations at acoustic and linguistic levels for both encoding and generation. We set up baseline systems consisting of a discrete speech encoder (returning pseudo-text units), a generative language model (trained on pseudo- text), and a speech decoder (generating a waveform from pseudo-text) all trained without supervision and validate the proposed metrics with human evaluation. Across 3 speech encoders (CPC, wav2vec 2.0, HuBERT), we find that the number of discrete units (50, 100, or 200) matters in a task-dependent and encoder- dependent way, and that some combinations approach text-based systems.1

2016

pdf
Radarly : écouter et analyser le web conversationnel en temps réel (Real time listening and analysis of the social web using Radarly)
Jade Copet | Christine de Carvalho | Virginie Mouilleron | Benoit Tabutiaux | Hugo Zanghi
Actes de la conférence conjointe JEP-TALN-RECITAL 2016. volume 5 : Démonstrations

De par le contexte conversationnel digital, l’outil Radarly a été conçu pour permettre de traiter de grands volumes de données hétérogènes en temps réel, de générer de nouveaux indicateurs et de les visualiser sur une interface cohérente et confortable afin d’en tirer des analyses et études pertinentes. Ce document expose les techniques et processus utilisés pour extraire et traiter toutes ces données.